from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting(against_lib="sklearnex", config="config.yml")
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.212	0.0	-1	1	NaN	0.045	0.001	0.245	0.245	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.518	0.0	-1	5	NaN	0.045	0.000	0.238	0.238	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.498	0.0	1	100	NaN	0.045	0.000	0.237	0.237	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.478	0.0	-1	100	NaN	0.045	0.000	0.238	0.238	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.458	0.0	1	5	NaN	0.045	0.000	0.237	0.237	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.0	NaN	7.548	0.0	1	1	NaN	0.044	0.000	0.238	0.238	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.406	0.0	-1	1	NaN	0.008	0.000	0.525	0.525	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.419	0.0	-1	5	NaN	0.007	0.000	0.514	0.514	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.411	0.0	1	100	NaN	0.008	0.000	0.516	0.516	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.414	0.0	-1	100	NaN	0.007	0.000	0.519	0.519	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.409	0.0	1	5	NaN	0.008	0.000	0.520	0.520	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.0	NaN	0.420	0.0	1	1	NaN	0.008	0.000	0.505	0.505	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.985	0.084	NaN	0.000	0.002	-1	1	0.663	0.171	0.003	11.623	11.625	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.003	NaN	0.000	0.022	-1	1	1.000	0.009	0.000	2.589	2.590	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.737	0.073	NaN	0.000	0.003	-1	5	0.757	0.172	0.002	15.875	15.876	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.003	NaN	0.000	0.023	-1	5	1.000	0.008	0.000	2.773	2.773	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.914	0.003	NaN	0.000	0.002	1	100	0.882	0.208	0.001	9.220	9.220	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	NaN	0.000	0.019	1	100	1.000	0.009	0.000	2.242	2.243	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.721	0.056	NaN	0.000	0.003	-1	100	0.882	0.209	0.001	13.023	13.024	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.003	NaN	0.000	0.023	-1	100	1.000	0.009	0.001	2.629	2.645	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.890	0.004	NaN	0.000	0.002	1	5	0.757	0.173	0.003	10.924	10.925	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	NaN	0.000	0.019	1	5	1.000	0.008	0.000	2.242	2.243	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.140	0.002	NaN	0.001	0.001	1	1	0.663	0.171	0.002	6.657	6.658	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.001	NaN	0.000	0.019	1	1	1.000	0.009	0.000	2.163	2.163	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.764	0.017	NaN	0.000	0.002	-1	1	0.896	0.025	0.000	70.335	70.340	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.002	NaN	0.000	0.004	-1	1	1.000	0.001	0.000	6.734	6.787	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.588	0.028	NaN	0.000	0.003	-1	5	0.922	0.026	0.000	100.061	100.067	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.005	NaN	0.000	0.006	-1	5	1.000	0.001	0.000	10.491	10.550	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.840	0.008	NaN	0.000	0.002	1	100	0.929	0.058	0.002	31.484	31.499	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	100	1.000	0.001	0.000	3.875	3.910	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.613	0.021	NaN	0.000	0.003	-1	100	0.929	0.058	0.000	45.268	45.269	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.004	NaN	0.000	0.006	-1	100	1.000	0.001	0.000	8.990	9.036	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.813	0.001	NaN	0.000	0.002	1	5	0.922	0.028	0.002	65.478	65.581	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	NaN	0.000	0.003	1	5	1.000	0.001	0.000	4.146	4.165	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.053	0.002	NaN	0.000	0.001	1	1	0.896	0.025	0.000	41.395	41.402	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	NaN	0.000	0.002	1	1	1.000	0.001	0.000	2.673	2.690	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.051	0.083	NaN	0.026	0.0	-1	1	NaN	0.757	0.048	4.029	4.038	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.844	0.041	NaN	0.021	0.0	-1	5	NaN	0.743	0.003	5.176	5.176	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.901	0.085	NaN	0.021	0.0	1	100	NaN	0.736	0.010	5.297	5.298	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.824	0.096	NaN	0.021	0.0	-1	100	NaN	0.745	0.012	5.130	5.131	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.596	0.080	NaN	0.022	0.0	1	5	NaN	0.726	0.012	4.952	4.952	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.735	0.131	NaN	0.021	0.0	1	1	NaN	0.746	0.009	5.009	5.009	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	NaN	0.021	0.0	-1	1	NaN	0.005	0.003	0.172	0.205	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	-1	5	NaN	0.003	0.003	0.180	0.240	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.029	0.0	1	100	NaN	0.001	0.001	0.502	0.674	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	-1	100	NaN	0.001	0.000	0.634	0.639	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	1	5	NaN	0.001	0.000	0.656	0.657	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	NaN	0.031	0.0	1	1	NaN	0.001	0.000	0.676	0.676	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.852	1.103	NaN	0.000	0.001	-1	1	0.929	0.116	0.003	7.334	7.337	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	1	1.000	0.000	0.000	10.082	10.631	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.133	0.526	NaN	0.000	0.001	-1	5	0.946	0.207	0.002	5.486	5.486	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	5	1.000	0.000	0.000	8.194	8.702	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.661	0.780	NaN	0.000	0.006	1	100	0.951	0.621	0.011	9.110	9.111	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	1	100	1.000	0.001	0.000	4.377	4.626	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.238	0.356	NaN	0.000	0.003	-1	100	0.951	0.611	0.007	5.298	5.298	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	NaN	0.000	0.005	-1	100	1.000	0.001	0.000	6.392	6.782	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.758	0.540	NaN	0.000	0.002	1	5	0.946	0.211	0.006	8.313	8.316	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.001	NaN	0.000	0.002	1	5	1.000	0.000	0.000	3.970	4.255	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.930	0.282	NaN	0.000	0.001	1	1	0.929	0.115	0.003	8.056	8.060	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	3.740	4.037	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.027	0.023	NaN	0.001	0.000	-1	1	0.891	0.000	0.000	66.295	66.843	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	1	1.000	0.000	0.000	24.164	25.156	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.024	0.001	NaN	0.001	0.000	-1	5	0.911	0.001	0.000	37.654	37.671	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	5	1.000	0.000	0.000	23.874	25.198	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.039	0.016	NaN	0.000	0.000	1	100	0.894	0.004	0.000	8.852	8.852	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	100	1.000	0.000	0.000	5.926	6.194	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.035	0.007	NaN	0.000	0.000	-1	100	0.894	0.005	0.000	6.757	6.774	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	100	1.000	0.000	0.000	20.687	21.822	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.021	0.000	NaN	0.001	0.000	1	5	0.911	0.001	0.000	31.911	31.921	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	5	1.000	0.000	0.000	6.503	6.897	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.019	0.000	NaN	0.001	0.000	1	1	0.891	0.000	0.000	48.408	48.491	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	NaN	0.000	0.001	1	1	1.000	0.000	0.000	6.745	7.155	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.598	0.243	30	0.027	0.0	random	NaN	0.366	0.031	1.633	1.639	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.598	0.119	30	0.027	0.0	k-means++	NaN	0.394	0.018	1.519	1.520	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.581	0.192	30	0.143	0.0	random	NaN	2.562	0.055	2.178	2.179	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.725	0.031	30	0.140	0.0	k-means++	NaN	2.717	0.050	2.107	2.108	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.010	0.000	random	0.001	0.0	0.0	9.009	12.907	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	1.000	0.0	0.0	8.755	14.661	See	See
4	KMeans_tall	predict	1000000	1000	2	0.001	0.000	30	0.011	0.000	k-means++	0.001	0.0	0.0	11.013	12.696	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	1.000	0.0	0.0	12.798	13.472	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.451	0.000	random	0.002	0.0	0.0	6.808	7.432	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	1.000	0.0	0.0	11.830	11.997	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.437	0.000	k-means++	0.002	0.0	0.0	6.962	7.554	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	1.000	0.0	0.0	13.569	14.012	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.074	0.003	20	0.002	0.0	random	NaN	0.026	0.002	2.860	2.870	See	See
3	KMeans_short	fit	10000	10000	2	0.206	0.002	20	0.001	0.0	k-means++	NaN	0.078	0.000	2.632	2.632	See	See
6	KMeans_short	fit	10000	10000	100	0.190	0.004	20	0.042	0.0	random	NaN	0.104	0.002	1.830	1.830	See	See
9	KMeans_short	fit	10000	10000	100	0.547	0.007	20	0.015	0.0	k-means++	NaN	0.280	0.004	1.952	1.952	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	adjusted_rand_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.010	0.000	random	0.000	0.000	0.0	4.027	4.038	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	1.000	0.000	0.0	12.651	12.985	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.001	0.000	0.0	3.847	3.873	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	1.000	0.000	0.0	12.938	13.332	See	See
7	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.344	0.000	random	0.279	0.001	0.0	2.583	2.594	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	1.000	0.000	0.0	9.159	9.305	See	See
10	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.344	0.000	k-means++	0.317	0.001	0.0	2.516	2.527	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	1.000	0.000	0.0	9.407	9.604	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	10.654	0.40	[20]	0.075	0.000	NaN	NaN	NaN	NaN	NaN	1.947	0.040	5.473	5.474	See	See
3	LogisticRegression	fit	1000	1000	10000	1.060	0.91	[26]	0.075	0.001	NaN	NaN	NaN	NaN	NaN	1.017	0.025	1.042	1.042	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.001	[20]	1.995	0.0	NaN	NaN	NaN	NaN	0.56	0.001	0.001	0.549	1.138	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.000	[20]	0.017	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.000	0.354	0.359	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.000	[26]	5.126	0.0	NaN	NaN	NaN	NaN	0.35	0.004	0.001	0.373	0.392	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.000	[26]	1.092	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.000	0.111	0.121	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.171	0.004	NaN	0.467	0.0	NaN	NaN	NaN	0.179	0.001	0.957	0.957	See	See
3	Ridge	fit	1000000	1000000	100	1.148	0.172	NaN	0.697	0.0	NaN	NaN	NaN	0.313	0.275	3.673	4.890	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.0	NaN	6.944	0.0	NaN	NaN	0.083	0.018	0.0	0.630	0.630	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	NaN	1.439	0.0	NaN	NaN	NaN	0.000	0.0	0.663	0.735	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	NaN	5.315	0.0	NaN	NaN	1.000	0.000	0.0	0.458	0.721	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	NaN	0.019	0.0	NaN	NaN	NaN	0.000	0.0	0.600	0.640	See	See